1. Technical Field
The present invention relates to a sensing system and a method thereof, in particular, to a compressive sensing system based on a personalized basis that first performs training to generate a personalized basis by using an original signal to maintain sparsity of a signal, so as to ensure that the original signal can be completely recovered and a method thereof.
2. Description of Related Art
In recent years, in the trend of an aging society and the increase of chronic diseases, there is a huge increase in requirements for home health care. How to consolidate a portable sensor and wireless communications to implement a wireless care system is exactly an important direction for implementing home health care. However, the wireless care system needs to detect various physiological signals continuously to provide real-time monitoring of a condition of a patient, and a large number of signals consume a large quantity of frequency bandwidths and power of the system, and therefore data needs to be compressed before transmission so as to improve a frequency bandwidth utilization rate. Generally, to compress data, compressive hardware is generally built in a conventional physiological signal sensor. However, the compressive hardware is too complex; consequently, the sensor consumes more power, and costs also become higher.
In view of this, a compressive sensing technology is put forward by a manufacturer to resolve the problem. The compressive sensing is to obtain a low-dimensional measurement value for a high-dimensional sparse signal by means of a sampling matrix. Therefore, the system only needs to use a low-dimensional signal as a transfer, and when necessary, reconstruct the low-dimensional sample into a high-dimensional signal by means of methods such as norm minimization. The compressive sensing has two features: (1) being capable of sampling at a frequency less than that of Nyquist Theorem, thereby reducing costs and power consumption of a digital analogue converter in a sensor; and (2) achieving a compressive effect during sampling without extra compressive hardware, thereby saving costs and power consumption of compressive hardware in a conventional sensor. However, a basis of the compressive sensing is built on sparsity of a signal, and the signal needs to be sparse enough to be recovered. Unfortunately, a conventional pre-constructed basis cannot make a physiological signal become sparse enough; when the signal is not sparse enough, a problem that the signal cannot be recovered into an original signal occurs.
Based on above, it can be known that a problem that when a signal is not sparse enough, the compressive sensing cannot recover the signal into an original signal has existed for a long time in the prior art. Therefore, it is really necessary to put forward a technical means for improvement to resolve the problem.